Center Loss: A Discriminative Feature Learning Approach for Deep Face Recognition（論文閱讀筆記）
摘要 卷積神經網絡(CNNs)在計算機視覺領域得到了廣泛的應用，極大地提高了計算機視覺領域的技術水平。在現有的神經網絡中，大多采用softmax損失作爲監督信號來訓練模型。爲了提高對深度學習特徵的識別能力，提出了一種新的人臉識別監控信號——center loss。具體來說，center loss同時學習每個類的深度特徵中心，並懲罰深度特徵與其對應的類中心之間的距離。更重要的是，我們證明了該
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相關文章
- 1. A Discriminative Feature Learning Approach for Deep Face Recognition
- 2. 《A Discriminative Feature Learning Approach for Deep Face Recognition》論文筆記
- 3. A Discriminative Feature Learning Approach for Deep Face Recognition 論文筆記
- 4. 論文筆記 | A Discriminative Feature Learning Approach for Deep Face Recognition
- 5. 文獻閱讀 - A Discriminative Feature Learning Approach for Deep Face Recognition
- 6. 閱讀A Discriminative Feature Learning Approach for Deep Face Recognition
- 7. 【人臉識別】A Discriminative Feature Learning Approach for Deep Face Recognition
- 8. 論文閱讀理解 - CenterLoss: A Discriminative Feature Learning Approach
- 9. 【論文整理】DNN-Face-Recognition-Papers Center Face (center loss)
- 10. centerloss系列文章筆記——人臉識別：A Discriminative Feature Learning Approach forDeep Face Recognition